Showing posts with label context. Show all posts
Showing posts with label context. Show all posts

Sunday, September 18, 2011

Providing some Context for looking at CPI charts

We have posted many charts this past week looking at CPI behavior through a variety of angles and lenses.  Our sources have been light on providing context and we have not filled in this area either.

So here is a first cut at the context that we were holding loosely in mind as we evaluated the incoming collection of charts and created a few of our own using the St. Louis Federal Reserves' FRED software.

To begin, CPI stands for Consumer Price Index.  In our view, the emphasis is on the Consumer (real people living out their lives) and the ways in which changes in Prices (specifically price inflation) might impact the standard of living for those people.

Central Questions:  The question we would like to be able to answer more fully is:
How does the cumulative change in prices affect the buying power of a range of different classes of consumers over time?
In particular, we want to be able to figure out to what degree the Cumulative Impact of Price Inflation changes each different class of consumer's standard of living over time.
Principles.   The underlying principle that we believe to be at work here is that: if cumulative CPI goes up relative to the wage trend for households representing a given class of consumers, then the Standard of Living for that class would go down in proportion.  We are interested in looking at the cumulative change over a variety of longer periods: 2 years, 3 years, 5 years, 10 years, 15 years, 20 years

A second key principle for us is that CPI Inflation is like Compound Interest; and so longer term cumulative views of the data should predominate.


Weaknesses of Traditional Reporting:  To us, the commonly used month over month results represent little more than noise since our focus is on cumulative results.

Even the very widely used year over year reporting is not particularly useful in this regard and is usually distracting at best (1 year effects are interesting but surely not the whole story) and misleading at worst as to the important long term inflation changes that are underway and the implications of those changes on real people's standards of living and quality of life.

A long standing general purpose admonition of ours is to Look at All the Data.   The traditional reporting on CPI violates this rule in spades with the focus on only headline CPI and core CPI .  CPI is an aggregate of aggregates of aggregates which averages out a huge degree of variation that is present in its sub-components.  To begin to understand the impact that cumulative price inflation has on Consumers (real people) we surely need to disaggregate the average CPI values and look individually at a good number of the key sub-components (e.g.  Food, Energy, Health Care, Rent, Transportation, Tuition) to see their cumulative price inflation over longer periods of time.   With few exceptions such as the excellent chart we posted earlier (A Unique View of CPI from Doug Short), almost all reportage we have found on CPI focuses on headline or core cpi.

Now it is true that the official report from the BLS (BLS August 2011 CPI news release)  does provide an exceptionally high degree of disaggregation in its tables by sub-categories and sub-sub-categories.  But there are no sub-component charts accompanying the BLS report, so looking at sub-component increases in cpi is left as a time-consuming exercise for the reader - an option that few have likely taken.    Furthermore, the data presented only covers the most recent 1 year period, so if you want to explore sub-category cumulative changes over periods of 2 to 20 years, you have to look somewhere else (e.g FRED or the BLS data base).

There is a lot of fruitful potential in this disaggregation, but I have not been able to find anyone so far other than Doug Short and our recent post (Drilling down into CPI 20 year trends)  tapping into this for more insights into understanding the cumulative impact of inflation on real people.  We will keep on looking and would love to hear about other work in progress studying the sub-components.

Core CPI interferes with clear thinking.  Finally, looking at widely discussed Core CPI and its many variants,  we see Core CPI as a serious impediment keeping us from thinking more clearly about the cumulative impact of price inflation on real people who all must eat and consume energy.  Rather than stripping food and energy out of the totals to create so-called CORE CPI, it would be much more useful in our estimation to include Food and include Energy as individual sub-categories - to look at the cumulative impact of Food price inflation and the cumulative impact of Energy price inflation as we have started to in our Drilling down into CPI 20 year trends  post.  And rather than lump all the rest of the categories together to create the "core", we would prefer to disaggregate these as well and look at them individually so we can discover patterns currently hidden..

A lot has been made about why Energy and Food are so volatile as the reason for going with Core CPI.  Our investigation shows that the mostly irrelevant month to month variations smooth out incredibly well for FOOD once we look at cumulative totals.  For ENERGY, there is clear volatility, but rather than hiding it, it's better to bring it out into the open.  And when we look at energy using a cumulative long term 5, 10, 15, 20 year approach, there is a lot less volatility than you might expect.  We'll have more on this later.

And the headline CPI number shows very low variability in trend when plotted using a long term cumulative view as we have shown in our early post ( Drilling down into CPI 20 year trends ).

There are further weaknesses we see in traditional CPI reporting regarding the limited number of different classes of consumers currently available) but we will save these for another day.

Now that we have some context on why we want to look at CPI, our plan is to revisit the CPI posts from earlier this past week.  We plan to update them by showing our thinking regarding what we see as the story that might be told for each chart in the context of the ideas we have explored in this post - real people, cumulative impact over longer time spans, disaggregation.  . 

Update 20 September 2011:  We have now updated all previous CPI posts with our further thoughts on what each chart is telling us.  Please check them out.


Friday, September 16, 2011

The impact of cpi changes

Calculated risk has an interesting post this morning that shows how CPI inflation values impact Social Security cost of living adjustments (COLA).    This is worth checking out as it adds some important context to why we might be interested cpi inflation in the first place.  The post includes lots of explanatory text regarding how all the pieces fit together to determine COLA changes.

Early Look: 2012 Social Security Cost-Of-Living Adjustment on track for 3.5% increase

Thursday, August 25, 2011

Creating the context for successful analyses

Context is essential.
If you just jump right into analysis of a complex data set and subsequent visualization and story telling without first establishing a proper context, you stand a good chance of misdirecting your focus, time, and energy.

If you are strong analyst, visualizer, and storyteller you can still end up with an interesting discovery, a good story, and exciting graphics to share. But you can have also missed hidden secrets that would have provided even greater understanding and value to your audience.

Forcing your audience to work harder. And worst of all, if you don't supply the context, you force your audience - every reader, viewer, listener, everyone who wants to interact with and learn from your visualization - to work harder with a lot of guess work and uncertainty to put what your findings into a useful perspective.

Templates shut down thinking. It is common to find examples of data analysis and visual reporting online with almost no context at all. For recurring analyses and reporting (such as various government monthly reports) it's also common to see the same basic template and boilerplate re-used verbatim month after month with no sign that any new or fresh thinking about how the context might have changed, or what had been learned in previous months, or how best to present that month's results for maximum clarity and ease of understanding.

Set the stage for discovery. What foundational context is it essential to establish in order to set the stage for the most successful exploratory analysis and discovery of new and surprising and useful domain trends, patterns, and exceptions?

Here are some ingredients that can help create a strong contextual foundation in a given data domain . These are especially important for recurring situations such as analyzing and reporting on employment/unemployment.

What's the central question? One of the best ways to supply context is to list up front the key questions that you hoped to answer as you started your analysis. Then in the storytelling and reporting that you create, make sure you establish a link back to these questions with any answers you have found, any surprises you discovered along the way, and any new questions you are keen to explore during the next round of analysis. In other words, show your thinking and link it back to the context your questions established.

Link to the mission. If the analysis you are doing is in support of an important mission, including a description of that mission and the vision and core values that the mission supports will add power and depth to the context of your work.

Think in advance of full set of key metrics. One other thing to note which we will return to in a future post is that there seems to be a connection between analyses that visualize the fewest domain metrics and the analyses that begin with the weakest contextual foundation.

What principles do you use to establish context for your data analysis/visualization/storytelling?

Monday, August 22, 2011

Reviewing the Situation

It's been a while since we formulated the Trend Visualization Principles displayed in our blog's right hand panel. So we thought it was a good time to revisit them and update them to reflect both on what we have learned and on the rapid pace of change in the intersecting domains of data gathering, analysis, visualization, story telling, sharing, and collaboration.

Our revised seven principles are:
  1. Context comes first
  2. Create a history
  3. Look at ALL the data
  4. Share ALL your data
  5. Explain your calculations
  6. Show your thinking
  7. Insure readability
Please check out the full statement in the right hand panel and let us know what you think.

These principles when put into practice can serve as antidotes to some of the weaknesses highlighted in the most recent post. (Common Weaknesses in Online Visualization & Storytelling)

We plan on posting some longer explanations of our thinking on these principles in coming days starting with two that are oft neglected in practice (self included) . Our experience has been that these two can reap substantial rewards when we make the time, when we take the time to remember to put them into action
  • Look at ALL the data
  • Show your thinking
What are your most useful principles that you keep in mind while gathering analyzing, visualizing, story telling, sharing, and collaborating about important trend data?